{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize\n",
    "\n",
    "Import packages and load data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'GradientBoostingClassifier' from 'sklearn.linear_model' (c:\\Users\\lc_zd\\.conda\\envs\\fdpredict\\lib\\site-packages\\sklearn\\linear_model\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\lc_zd\\dev\\distress-prediction\\notebooks\\xgboost.ipynb Cell 2'\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/lc_zd/dev/distress-prediction/notebooks/xgboost.ipynb#ch0000001?line=4'>5</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mimblearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mover_sampling\u001b[39;00m \u001b[39mimport\u001b[39;00m BorderlineSMOTE\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/lc_zd/dev/distress-prediction/notebooks/xgboost.ipynb#ch0000001?line=5'>6</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mscipy\u001b[39;00m \u001b[39mimport\u001b[39;00m stats\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/lc_zd/dev/distress-prediction/notebooks/xgboost.ipynb#ch0000001?line=6'>7</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlinear_model\u001b[39;00m \u001b[39mimport\u001b[39;00m GradientBoostingClassifier\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/lc_zd/dev/distress-prediction/notebooks/xgboost.ipynb#ch0000001?line=7'>8</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetrics\u001b[39;00m \u001b[39mimport\u001b[39;00m auc, roc_curve\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/lc_zd/dev/distress-prediction/notebooks/xgboost.ipynb#ch0000001?line=8'>9</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmodel_selection\u001b[39;00m \u001b[39mimport\u001b[39;00m train_test_split\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'GradientBoostingClassifier' from 'sklearn.linear_model' (c:\\Users\\lc_zd\\.conda\\envs\\fdpredict\\lib\\site-packages\\sklearn\\linear_model\\__init__.py)"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as py\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import BorderlineSMOTE\n",
    "from scipy import stats\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import auc, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "py.init_notebook_mode()\n",
    "dataset = pandas.read_csv(\"../data/data.csv\", sep=\"\\t\")\n",
    "# dataset = dataset[dataset['accper'] > \"2017-01-01\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set factors (deps)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accounting_factors = [\n",
    "    \"Size\",\n",
    "    \"Growth\",\n",
    "    \"Lev\",\n",
    "    \"FCF\",\n",
    "    \"PPE\",\n",
    "    \"ROA\",\n",
    "    \"ROE\",\n",
    "    \"TobinQ\",\n",
    "    \"Top1\",\n",
    "    \"Top10\",\n",
    "    \"Inventory\",\n",
    "    \"InvTurnover\",\n",
    "    \"lnBoard\",\n",
    "    \"pctIndepen\",\n",
    "    \"Age\",\n",
    "    \"Big4\",\n",
    "    \"DUAL\",\n",
    "    \"SOE\",\n",
    "    \"Loss\",\n",
    "    \"DvdPayout\",\n",
    "    \"Quick\",\n",
    "    \"Current\",\n",
    "    \"ICRebitda\",\n",
    "]\n",
    "\n",
    "market_factors = [\n",
    "    \"ret\",\n",
    "    \"annualret\",\n",
    "    \"volatility\",\n",
    "    \"turnover\",\n",
    "    \"PE\",\n",
    "    \"institute\",\n",
    "    \"coverage\",\n",
    "    \"brokers\",\n",
    "    \"MV\",\n",
    "]\n",
    "\n",
    "academic_factors = [\n",
    "    \"avLoss\",\n",
    "    \"NCSKEW\",\n",
    "    \"DUVOL\",\n",
    "    \"KZologit\",\n",
    "    \"SA\",\n",
    "    \"DA\",\n",
    "    \"REM\",\n",
    "    \"PLDdummy\",\n",
    "    \"ETR1\",\n",
    "    \"BTD\",\n",
    "    \"RiskT\",\n",
    "    \"avgLawTax\",\n",
    "    \"RPTratio\",\n",
    "]\n",
    "\n",
    "factors = accounting_factors + market_factors + academic_factors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set predicted variable (indep)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = \"preFD_e\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 18194, 1: 2860})\n",
      "Counter({1: 18194, 0: 18194})\n"
     ]
    }
   ],
   "source": [
    "X = stats.zscore(dataset[factors])\n",
    "y = dataset[predicted]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "\n",
    "oversample = BorderlineSMOTE()\n",
    "print(Counter(y_train))\n",
    "X_train, y_train = oversample.fit_resample(X_train, y_train)\n",
    "print(Counter(y_train))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = GradientBoostingClassifier()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12610916, 0.87389084],\n",
       "       [0.72888238, 0.27111762],\n",
       "       [0.87416261, 0.12583739],\n",
       "       ...,\n",
       "       [0.28033057, 0.71966943],\n",
       "       [0.08304232, 0.91695768],\n",
       "       [0.18556912, 0.81443088]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob = lr.predict_proba(X_test)\n",
    "prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preFD_e\n"
     ]
    },
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     "data": {
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       "0.9317821827840638"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(predicted)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, prob[:, 1], pos_label=1)\n",
    "auc(fpr, tpr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       "                            \n",
       "var gd = document.getElementById('a1071aa1-cae8-4a01-9c3f-655e89ddda8e');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc = go.Scatter(\n",
    "    x=fpr,\n",
    "    y=tpr,\n",
    "    name=\"roc\",\n",
    "    showlegend=True,\n",
    "    marker={\"color\": \"#004777\"},\n",
    ")\n",
    "data = [roc]    \n",
    "layout = go.Layout(\n",
    "    title=f\"ROC\", width=600, height=600\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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